DynaProg: Deterministic Dynamic Programming solver for finite horizon multi-stage decision problems

DynaProg is an open-source MATLAB toolbox for solving multi-stage deterministic optimal decision problems using Dynamic Programming. This class of optimal control problems can be solved with Dynamic Programming (DP), which is a well-established optimal control technique suited for highly non-linear dynamic systems. Unfortunately, the numerical implementation of Dynamic Programming can be challenging and time consuming, which may discourage researchers from adopting it. The toolbox addresses these issues by providing a numerically fast DP optimization engine wrapped in a simple interface that allows the user to set up an optimal control problem in a straightforward yet flexible environment, with no restrictions on the controlled system’s simulation model. Therefore, it enables researchers to easily explore the usage of Dynamic Programming in their fields of expertise. Thorough documentation and a set of step-by-step examples complete the toolbox, thus allowing for easy deployment and providing insight of the optimization engine. Finally, the source code’s classoriented design allows researchers experienced in Dynamic Programming to extend the toolbox if needed

Development of a pressure-based technique to control IMEP and MFB50 in a 3.0L diesel engine

Abstract A pressure-based technique for the control of IMEP (Indicated Mean Effective Pressure) and MFB50 (crank angle at which 50% of fuel mass fraction has burned) has been developed, assessed and tested by means of MiL (Model-in-the-Loop) on a 4 cylinder 3.0L Euro VI diesel engine. The activity was carried out in the frame of a research project in collaboration with FPT Industrial. The developed controller is of the closed-loop type. It receives, as input, the desired targets of IMEP and MFB50 for each cycle and cylinder and performs a cycle-by-cycle and cylinder-to-cylinder correction of the injected fuel quantity of the main pulse (qmain) and of the start of injection of the main pulse (SOImain), in order to reduce the deviation between the actual and target values of IMEP and MFB50, respectively. The method is referred to as "pressure-based" since it requires the measurement of the in-cylinder pressure trace for each cylinder in order to extract the actual values of IMEP and MFB50. In fact, the actual IMEP value can be estimated by integrating the pressure signal with respect to the in-cylinder volume. At the same time, the actual MFB50 value can be extracted from the heat release curve, which is obtained from the in-cylinder pressure trace by using a single-zone heat release model. The proposed control technique has been developed in Simulink environment, and has been assessed and tested on an engine emulator which is constituted by a GT-power model of the 3.0L diesel engine. The controller has been tested in transient operation over a load ramp profile at different engine speeds and over a WHTC interval, and demonstrated to have a good potential for IMEP and MFB50 control, since it is characterized by a fast response and a limited overshoot behavior

Evaluation of the Environmental Benefit of an Eco-design Strategy on the Life Cycle Assessment of a Permanent Magnet Synchronous High-speed Electric Motor

The purpose of this study is to assess the environmental impacts of a Permanent Magnet Synchronous high-speed electric Motor (PMSM) and quantify the environmental savings resulting from the adoption of an eco-design strategy. To this end, a PMSM was identified as a baseline for the study, and a cradle-to-grave environmental life cycle assessment was performed based on primary data. The environmental impacts of the baseline PMSM were compared to those of its lightweighted version, which was obtained by adopting an eco-design strategy focused on aluminum content reduction. Other eco-design strategies were investigated to evaluate additional savings. The results obtained for the baseline PMSM reveal that the procurement and processing of raw materials contribute the most to all impact categories. The results obtained for the lightweighted PMSM reveal that, while reducing the aluminum content does not significantly lower the impacts in any of the most relevant impact categories, reducing the copper content would significantly reduce the use of mineral and metal resources. Lastly, eco-design strategies that focus on permanent magnets would significantly reduce climate change

Impact of Predictive Battery Thermal Management for a 48V Hybrid Electric Vehicle

Overheating of battery packs in electrified vehicles is detrimental to their lifetime and performance. Unfortunately, designing a control strategy that ensures battery protection without jeopardizing fuel economy is not a straightforward task. In this paper, we investigate battery temperature-sensitive optimal energy management for a 48V mild-hybrid electric vehicle to prevent overheating with minimal fuel consumption increase. Indeed, this family of hybrid architectures is challenging due to the absence of an active cooling system.In particular, we modeled a p0 parallel-hybrid with a 48V battery pack and we employed dynamic programming to numerically investigate the fuel economy capability while tracking the battery pack temperature.First, we tuned a battery current-constrained powertrain control strategy in order to avoid battery overheating, which could be easily implemented on-board. Then, we implemented a predictive temperature-constrained strategy that exploits the a priori knowledge of driving conditions and temperature constraints to maximize fuel economy.Results show that both strategies are able to meet the battery temperature constraints, although the predictive temperature-constrained control strategy outperforms the current-constrained strategy in terms of fuel economy. This case study demonstrates the theoretical benefits of a predictive battery thermal management for 48V mild hybrids

End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe

A cradle-to-grave life cycle assessment focused on end-of-life (EoL) was conducted in this study for three configurations of a light-duty commercial vehicle (LDCV): diesel, compressed natural gas (CNG), and battery electric vehicle (BEV). The aim is to investigate the impact of recycling under two EoL scenarios with different allocation methods. The first is based on the traditional avoided burden method, while the second is based on the circular footprint formula (CFF) developed by the European Commission. For each configuration, a detailed multilevel waste management scheme was developed in compliance with the 2000/53/CE directive and ISO22628 standard. The results showed that the global warming potential (GWP) impact under the CFF method is significantly greater when compared to the avoided burden method because of the A-parameter, which allocates the burdens and benefits between the two connected product systems. Furthermore, in all configurations and scenarios, the benefits due to the avoided production of virgin materials compensate for the recycling burdens within GWP impact. The main drivers of GWP reduction are steel recycling for all of the considered LDCVs, platinum, palladium, and rhodium recycling for the diesel and CNG configurations, and Li-ion battery recycling for the BEV configuration. Finally, the EoL stage significantly reduces the environmental impact of those categories other than GWP

Electrified road transport through plug-in hybrid powertrains: Compliance by simulation of CO2 specific emission targets with real driving cycles

Worldwide targets on specific CO2 emissions (g/km) seem to make the use of internal combustion engines (ICE) prohibitive when adopting conventional driving cycles concerning road transport. This research comes therefore from the necessity of an accurate analysis of the real driving habits in order to evaluate whether its implementation on an alternative powertrain, suitable to differentiate urban (local zero emissions) and extra-urban travels (highest performances of ICEs, even better than electric motors when contemplating the entire energy chain), can guarantee the compliance with specific CO2 emissions reduction legislation; this last has been introduced with the aim of containing or even erasing global emissions from the transport sector in next years. After an overview of all the main available technological alternatives, as regards powertrains, the Plug-in Hybrid (PHEV) solution has been analysed. An experimental driving cycle is proposed by combining representative cycles obtained from a previous study, based on data provided by FCA, now Stellantis, where a clustering procedure has been applied to a sample of over two-thousand real journeys made in 2015 and 2016 in all Europe with conventional automobiles; appropriate ranges of distance, time, average speed in urban and extra urban conditions, idle times and stops have been identified thanks to a statistical analysis and the cycle has been created with all of these requirements to be as similar as possible to most of daily trips by road transport. PHEV market has been examined in order to identify the components and architectures that characterize the most registered automobiles; a realistic model has therefore been created and used for the experimental cycle simulation. Simulation results show that PHEV technology has the potential to consume 69% less fuel than a conventional vehicle counterpart with a consequent reduction of 71% in emitted tank-to-wheel (TTW) tons of CO2 and significant reductions in fuel expenditure, in one year, because of the different source of energy

Life Cycle Assessment of an NMC battery for application to electric light-duty commercial vehicles and comparison with a sodium-nickel-chloride battery

This paper presents the results of an environmental assessment of a Nickel-Manganese-Cobalt (NMC) Lithium-ion traction battery for Battery Electric Light-Duty Commercial Vehicles (BEV-LDCV) used for urban and regional freight haulage. A cradle-to-grave Life Cycle Inventory (LCI) of NMC111 is provided, operation and end-of-life stages are included, and insight is also given into a Life Cycle Assessment of different NMC chemistries. The environmental impacts of the manufacturing stages of the NMC111 battery are then compared with those of a Sodium-Nickel-Chloride (ZEBRA) battery. In the second part of the work, two electric-battery LDCVs (powered with NMC111 and ZEBRA batteries, respectively) and a diesel urban LDCV are analysed, considering a wide set of environmental impact categories. The results show that the NMC111 battery has the highest impacts from production in most of the impact categories. Active cathode material, Aluminium, Copper, and energy use for battery production are the main contributors to the environmental impact. However, when vehicle application is investigated, NMC111-BEV shows lower environmental impacts, in all the impact categories, than ZEBRA-BEV. This is mainly due to the greater efficiency of the NMC111 battery during vehicle operation. Finally, when comparing BEVs to a diesel LDCV, the electric powertrains show advantages over the diesel one as far as global warming, abiotic depletion potential-fossil fuels, photochemical oxidation, and ozone layer depletion are concerned. However, the diesel LDCV performs better in almost all the other investigated impact categories

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered

Assessing lightweight layouts for a parallel Hybrid Electric Vehicle driveline

The presence of multiple power sources and the several possible architectures that can be designed when referring to hybrid electric vehicle (HEV) powertrains complicate the identification of an optimal HEV configuration. Among the diverse parameters that can be chosen in design and sizing processes of a parallel full HEV, the number of gears and the gear ratios in the transmission are considered as fulcra of this case study. For this scope, five different transmissions have been sized while assessing drivability and acceleration performance along with the fuel economy capability. A dynamic programming-based approach algorithm has been utilized for controlling the HEV, thus providing reliable outcomes and enhancing the consistency of the study. The results obtained in the sizing process suggest that the presence of an electric machine may mitigate the effect of the lower number of gears and enhance the fuel consumption efficiency even when reducing the number of gears in the transmission to 2 or 3. More precisely, even though they might be associated to slightly higher fuel consumption and, in turn, operative costs compared with the other considered configurations, these drawbacks can be overcome by the higher savings in production costs, thus suggesting parallel full HEVs with a reduced number of gears as an appealing design option